{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to language-model-based data augmentation (LAMBADA)\n",
    "https://arxiv.org/pdf/1911.03118.pdf\n",
    "\n",
    "LAMBADA (Anaby-Tavor et al., 2019) is proposed to generate synthetic data. We follow the approach with modification into nlpaug so that we can generate more data with few lines of code. The following figures show steps of training LAMBADA. We will go through it step by step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='../res/lambada_algo.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 0: Input Data\n",
    "expected column name are \"text\" and \"label\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>My hope lay in Jack's promise that he would ke...</td>\n",
       "      <td>LABEL_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dotty continued to go to Mrs. Gray's every nig...</td>\n",
       "      <td>LABEL_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>It was a bright and cheerful scene that greete...</td>\n",
       "      <td>LABEL_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cell division is the process by which a parent...</td>\n",
       "      <td>LABEL_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Debugging is the process of finding and resolv...</td>\n",
       "      <td>LABEL_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>To explain transitivity, let us look first at ...</td>\n",
       "      <td>LABEL_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Milka and John are playing in the garden. Her ...</td>\n",
       "      <td>LABEL_2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text    label\n",
       "0  My hope lay in Jack's promise that he would ke...  LABEL_0\n",
       "1  Dotty continued to go to Mrs. Gray's every nig...  LABEL_1\n",
       "2  It was a bright and cheerful scene that greete...  LABEL_0\n",
       "3  Cell division is the process by which a parent...  LABEL_2\n",
       "4  Debugging is the process of finding and resolv...  LABEL_1\n",
       "5  To explain transitivity, let us look first at ...  LABEL_2\n",
       "6  Milka and John are playing in the garden. Her ...  LABEL_2"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv('../test/res/text/classification.csv')\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 1: Train the classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m W&B installed but not logged in.  Run `wandb login` or set the WANDB_API_KEY env variable.\n",
      "Traceback (most recent call last):\n",
      "  File \"../scripts/lambada/train_cls.py\", line 79, in <module>\n",
      "    main(args)\n",
      "  File \"../scripts/lambada/train_cls.py\", line 55, in main\n",
      "    baseline_estimator = make_baseline_estimator(args, train_data, val_data)\n",
      "  File \"../scripts/lambada/train_cls.py\", line 18, in make_baseline_estimator\n",
      "    model = ClassificationModel(\n",
      "  File \"/home/edward/anaconda3/lib/python3.8/site-packages/simpletransformers/classification/classification_model.py\", line 194, in __init__\n",
      "    raise ValueError(\n",
      "ValueError: 'use_cuda' set to True when cuda is unavailable. Make sure CUDA is available or set use_cuda=False.\n"
     ]
    }
   ],
   "source": [
    "!python ../scripts/lambada/train_cls.py  \\\n",
    "    --train_data_path ../test/res/text/classification.csv \\\n",
    "    --val_data_path ../test/res/text/classification.csv \\\n",
    "    --output_dir ../model/lambada/cls \\\n",
    "    --device cuda \\\n",
    "    --num_epoch 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 2a: Processing data for task-adaptive pretraining"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "!python ../scripts/lambada/data_processing.py \\\n",
    "    --data_path ../test/res/text/classification.csv \\\n",
    "    --output_dir ../test/res/text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 2b: Task-adpative pretraining for langauge model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using the `WAND_DISABLED` environment variable is deprecated and will be removed in v5. Use the --report_to flag to control the integrations used for logging result (for instance --report_to none).\n",
      "07/15/2021 22:52:26 - WARNING - __main__ -   Process rank: -1, device: cpu, n_gpu: 0distributed training: False, 16-bits training: False\n",
      "07/15/2021 22:52:26 - INFO - __main__ -   Training/evaluation parameters TrainingArguments(\n",
      "_n_gpu=0,\n",
      "adafactor=False,\n",
      "adam_beta1=0.9,\n",
      "adam_beta2=0.999,\n",
      "adam_epsilon=1e-08,\n",
      "dataloader_drop_last=False,\n",
      "dataloader_num_workers=0,\n",
      "dataloader_pin_memory=True,\n",
      "ddp_find_unused_parameters=None,\n",
      "debug=[],\n",
      "deepspeed=None,\n",
      "disable_tqdm=False,\n",
      "do_eval=False,\n",
      "do_predict=False,\n",
      "do_train=True,\n",
      "eval_accumulation_steps=None,\n",
      "eval_steps=500,\n",
      "evaluation_strategy=IntervalStrategy.NO,\n",
      "fp16=False,\n",
      "fp16_backend=auto,\n",
      "fp16_full_eval=False,\n",
      "fp16_opt_level=O1,\n",
      "gradient_accumulation_steps=1,\n",
      "greater_is_better=None,\n",
      "group_by_length=False,\n",
      "ignore_data_skip=False,\n",
      "label_names=None,\n",
      "label_smoothing_factor=0.0,\n",
      "learning_rate=5e-05,\n",
      "length_column_name=length,\n",
      "load_best_model_at_end=False,\n",
      "local_rank=-1,\n",
      "log_level=-1,\n",
      "log_level_replica=-1,\n",
      "log_on_each_node=True,\n",
      "logging_dir=runs/Jul15_22-52-26_DESKTOP-TSB80R7,\n",
      "logging_first_step=False,\n",
      "logging_steps=500,\n",
      "logging_strategy=IntervalStrategy.STEPS,\n",
      "lr_scheduler_type=SchedulerType.LINEAR,\n",
      "max_grad_norm=1.0,\n",
      "max_steps=-1,\n",
      "metric_for_best_model=None,\n",
      "mp_parameters=,\n",
      "no_cuda=False,\n",
      "num_train_epochs=2.0,\n",
      "output_dir=../model/lambada/gen,\n",
      "overwrite_output_dir=True,\n",
      "past_index=-1,\n",
      "per_device_eval_batch_size=4,\n",
      "per_device_train_batch_size=4,\n",
      "prediction_loss_only=False,\n",
      "push_to_hub=False,\n",
      "push_to_hub_model_id=gen,\n",
      "push_to_hub_organization=None,\n",
      "push_to_hub_token=None,\n",
      "remove_unused_columns=True,\n",
      "report_to=['tensorboard'],\n",
      "resume_from_checkpoint=None,\n",
      "run_name=../model/lambada/gen,\n",
      "save_steps=10000,\n",
      "save_strategy=IntervalStrategy.STEPS,\n",
      "save_total_limit=None,\n",
      "seed=42,\n",
      "sharded_ddp=[],\n",
      "skip_memory_metrics=True,\n",
      "tpu_metrics_debug=False,\n",
      "tpu_num_cores=None,\n",
      "use_legacy_prediction_loop=False,\n",
      "warmup_ratio=0.0,\n",
      "warmup_steps=0,\n",
      "weight_decay=0.0,\n",
      ")\n",
      "07/15/2021 22:52:27 - WARNING - datasets.builder -   Using custom data configuration default-26b7935ec85772ea\n",
      "Downloading and preparing dataset text/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/edward/.cache/huggingface/datasets/text/default-26b7935ec85772ea/0.0.0/e16f44aa1b321ece1f87b07977cc5d70be93d69b20486d6dacd62e12cf25c9a5...\n",
      "Dataset text downloaded and prepared to /home/edward/.cache/huggingface/datasets/text/default-26b7935ec85772ea/0.0.0/e16f44aa1b321ece1f87b07977cc5d70be93d69b20486d6dacd62e12cf25c9a5. Subsequent calls will reuse this data.\n",
      "[INFO|configuration_utils.py:530] 2021-07-15 22:52:27,965 >> loading configuration file https://huggingface.co/gpt2/resolve/main/config.json from cache at /home/edward/.cache/huggingface/transformers/fc674cd6907b4c9e933cb42d67662436b89fa9540a1f40d7c919d0109289ad01.7d2e0efa5ca20cef4fb199382111e9d3ad96fd77b849e1d4bed13a66e1336f51\n",
      "[INFO|configuration_utils.py:566] 2021-07-15 22:52:27,966 >> Model config GPT2Config {\n",
      "  \"activation_function\": \"gelu_new\",\n",
      "  \"architectures\": [\n",
      "    \"GPT2LMHeadModel\"\n",
      "  ],\n",
      "  \"attn_pdrop\": 0.1,\n",
      "  \"bos_token_id\": 50256,\n",
      "  \"embd_pdrop\": 0.1,\n",
      "  \"eos_token_id\": 50256,\n",
      "  \"gradient_checkpointing\": false,\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"layer_norm_epsilon\": 1e-05,\n",
      "  \"model_type\": \"gpt2\",\n",
      "  \"n_ctx\": 1024,\n",
      "  \"n_embd\": 768,\n",
      "  \"n_head\": 12,\n",
      "  \"n_inner\": null,\n",
      "  \"n_layer\": 12,\n",
      "  \"n_positions\": 1024,\n",
      "  \"resid_pdrop\": 0.1,\n",
      "  \"scale_attn_weights\": true,\n",
      "  \"summary_activation\": null,\n",
      "  \"summary_first_dropout\": 0.1,\n",
      "  \"summary_proj_to_labels\": true,\n",
      "  \"summary_type\": \"cls_index\",\n",
      "  \"summary_use_proj\": true,\n",
      "  \"task_specific_params\": {\n",
      "    \"text-generation\": {\n",
      "      \"do_sample\": true,\n",
      "      \"max_length\": 50\n",
      "    }\n",
      "  },\n",
      "  \"transformers_version\": \"4.8.0\",\n",
      "  \"use_cache\": true,\n",
      "  \"vocab_size\": 50257\n",
      "}\n",
      "\n",
      "[INFO|tokenization_auto.py:427] 2021-07-15 22:52:27,966 >> Could not locate the tokenizer configuration file, will try to use the model config instead.\n",
      "[ERROR|configuration_utils.py:511] 2021-07-15 22:52:27,966 >> file ../model/lambada/cls/config.json not found\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/edward/anaconda3/envs/py39/lib/python3.9/site-packages/transformers/configuration_utils.py\", line 497, in get_config_dict\n",
      "    resolved_config_file = cached_path(\n",
      "  File \"/home/edward/anaconda3/envs/py39/lib/python3.9/site-packages/transformers/file_utils.py\", line 1344, in cached_path\n",
      "    raise EnvironmentError(f\"file {url_or_filename} not found\")\n",
      "OSError: file ../model/lambada/cls/config.json not found\n",
      "\n",
      "During handling of the above exception, another exception occurred:\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"/mnt/c/edward/coding/public/nlpaug_dev/example/../scripts/lambada/run_clm.py\", line 492, in <module>\n",
      "    main()\n",
      "  File \"/mnt/c/edward/coding/public/nlpaug_dev/example/../scripts/lambada/run_clm.py\", line 307, in main\n",
      "    tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, **tokenizer_kwargs)\n",
      "  File \"/home/edward/anaconda3/envs/py39/lib/python3.9/site-packages/transformers/models/auto/tokenization_auto.py\", line 529, in from_pretrained\n",
      "    config = AutoConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)\n",
      "  File \"/home/edward/anaconda3/envs/py39/lib/python3.9/site-packages/transformers/models/auto/configuration_auto.py\", line 446, in from_pretrained\n",
      "    config_dict, _ = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs)\n",
      "  File \"/home/edward/anaconda3/envs/py39/lib/python3.9/site-packages/transformers/configuration_utils.py\", line 517, in get_config_dict\n",
      "    raise EnvironmentError(msg)\n",
      "OSError: Can't load config for '../model/lambada/cls'. Make sure that:\n",
      "\n",
      "- '../model/lambada/cls' is a correct model identifier listed on 'https://huggingface.co/models'\n",
      "\n",
      "- or '../model/lambada/cls' is the correct path to a directory containing a config.json file\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!source activate py39; python ../scripts/lambada/run_clm.py \\\n",
    "    --tokenizer_name ../model/lambada/cls \\\n",
    "    --model_name_or_path gpt2 \\\n",
    "    --model_type gpt2 \\\n",
    "    --train_file ../test/res/text/mlm_data.txt \\\n",
    "    --output_dir ../model/lambada/gen \\\n",
    "    --do_train \\\n",
    "    --overwrite_output_dir \\\n",
    "    --per_device_train_batch_size 4 \\\n",
    "    --per_device_eval_batch_size 4 \\\n",
    "    --save_steps=10000 \\\n",
    "    --num_train_epochs 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 3 ~ 5: Generate synthetic data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nlpaug.augmenter.sentence as nas\n",
    "aug = nas.LambadaAug(model_dir='../model/lambada', threshold=0.3, batch_size=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>The truth is shake hands with an assembly of...</td>\n",
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       "      <td>LABEL_1</td>\n",
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       "      <th>13</th>\n",
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       "      <th>15</th>\n",
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       "      <td>The reader may be heated to the lungs of coa...</td>\n",
       "      <td>LABEL_1</td>\n",
       "      <td>LABEL_1</td>\n",
       "      <td>0.9999</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>The journeyless blue and gives every indicat...</td>\n",
       "      <td>LABEL_1</td>\n",
       "      <td>LABEL_1</td>\n",
       "      <td>0.9999</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
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       "      <td>LABEL_1</td>\n",
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       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>The molecules did not seem to notice any one...</td>\n",
       "      <td>LABEL_2</td>\n",
       "      <td>LABEL_2</td>\n",
       "      <td>1.0000</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>The first digital networks are mainly used f...</td>\n",
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       "      <td>LABEL_2</td>\n",
       "      <td>1.0000</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>The human heart was seldom very well. He was...</td>\n",
       "      <td>LABEL_2</td>\n",
       "      <td>LABEL_2</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>The human body is vital for the first place ...</td>\n",
       "      <td>LABEL_2</td>\n",
       "      <td>LABEL_2</td>\n",
       "      <td>0.9998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>The biggest desert of fruits I could see in ...</td>\n",
       "      <td>LABEL_2</td>\n",
       "      <td>LABEL_2</td>\n",
       "      <td>0.9953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>35</td>\n",
       "      <td>The first trip was in the summer. Later, she...</td>\n",
       "      <td>LABEL_3</td>\n",
       "      <td>LABEL_3</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>36</td>\n",
       "      <td>When at the upper end of the upper end of th...</td>\n",
       "      <td>LABEL_3</td>\n",
       "      <td>LABEL_3</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>37</td>\n",
       "      <td>The human body is made of warm animals, like...</td>\n",
       "      <td>LABEL_3</td>\n",
       "      <td>LABEL_3</td>\n",
       "      <td>1.0000</td>\n",
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       "      <th>39</th>\n",
       "      <td>39</td>\n",
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       "      <td>LABEL_3</td>\n",
       "      <td>LABEL_3</td>\n",
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      ],
      "text/plain": [
       "    id                                               text    label     pred  \\\n",
       "10  10    The truth is shake hands with an assembly of...  LABEL_1  LABEL_1   \n",
       "13  13    The first digital networks are imported from...  LABEL_1  LABEL_1   \n",
       "15  15    The National Sleep bird on the River of the ...  LABEL_1  LABEL_1   \n",
       "17  17    The reader may be heated to the lungs of coa...  LABEL_1  LABEL_1   \n",
       "18  18    The journeyless blue and gives every indicat...  LABEL_1  LABEL_1   \n",
       "19  19    The first magnitude Greeks is also disputed ...  LABEL_1  LABEL_1   \n",
       "20  20    The molecules did not seem to notice any one...  LABEL_2  LABEL_2   \n",
       "21  21    The first digital networks are mainly used f...  LABEL_2  LABEL_2   \n",
       "23  23    The human heart was seldom very well. He was...  LABEL_2  LABEL_2   \n",
       "26  26    The human body is vital for the first place ...  LABEL_2  LABEL_2   \n",
       "28  28    The biggest desert of fruits I could see in ...  LABEL_2  LABEL_2   \n",
       "35  35    The first trip was in the summer. Later, she...  LABEL_3  LABEL_3   \n",
       "36  36    When at the upper end of the upper end of th...  LABEL_3  LABEL_3   \n",
       "37  37    The human body is made of warm animals, like...  LABEL_3  LABEL_3   \n",
       "39  39    The first French instant, was most remarkabl...  LABEL_3  LABEL_3   \n",
       "\n",
       "      prob  \n",
       "10  0.9999  \n",
       "13  0.9999  \n",
       "15  0.9996  \n",
       "17  0.9999  \n",
       "18  0.9999  \n",
       "19  0.9999  \n",
       "20  1.0000  \n",
       "21  1.0000  \n",
       "23  1.0000  \n",
       "26  0.9998  \n",
       "28  0.9953  \n",
       "35  1.0000  \n",
       "36  1.0000  \n",
       "37  1.0000  \n",
       "39  1.0000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aug.augment(['LABEL_0', 'LABEL_1', 'LABEL_2', 'LABEL_3'], n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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